README.md

---
license: cc-by-4.0
task_categories:
- video-text-to-text
- visual-question-answering
language:
- en
size_categories:
- <1K
configs:
- config_name: default
  data_files:
  - split: validation
    path: validation_dataset.parquet
---

# QuantiPhy (Validation Set)

## Dataset Summary

**QuantiPhy** is a benchmark for evaluating whether vision–language models (VLMs) can perform **quantitative physical inference** from visual evidence, rather than producing plausible but ungrounded numerical guesses.

This repository contains the **official validation set** of QuantiPhy, released to support **model development, ablation studies, and preliminary evaluation**.  
The validation set represents approximately **4% of the full benchmark** and consists of **159 video–question–answer (QA) pairs**.

Each instance requires a model to output a **single continuous numerical value** (e.g., object size, velocity, or acceleration) in real-world units, given a short video and a natural-language question.

---

## Intended Use

This validation release is intended for:
- model debugging and prompt development,
- hyperparameter tuning,
- ablation and error analysis,
- sanity checks prior to full benchmark evaluation.

It is **not intended** to be used as a substitute for the full QuantiPhy benchmark.  
The complete dataset, including training and test splits, will be released separately.

---

## Supported Tasks

- **Video-based numerical regression**
- **Quantitative visual reasoning**
- **Vision–language model evaluation**

Tasks cover three core kinematic properties:
- **Size**
- **Velocity**
- **Acceleration**

All questions are **open-ended** and require predicting a real-valued scalar.

---

## Dataset Structure

Each instance is represented as a structured video–text record with the following fields:

| Field | Description |
|---|---|
| `video_id` | Unique identifier for the video |
| `video_source` | Data source (`simulation`, `lab`, or `internet`) |
| `video_type` | Four-character code encoding task configuration |
| `fps` | Frame rate of the video |
| `inference_type` | Static or dynamic prior/target configuration |
| `question` | Natural-language question with explicit physical units |
| `prior` | Physical prior provided in world units (e.g., object size, velocity, or acceleration) |
| `depth_info` | Depth/distance information for 3D configurations (if applicable) |
| `answer` | Ground-truth numerical value (float, real-world units) |

Videos are short (typically **2–3 seconds**) and recorded with a **static camera** to ensure well-defined kinematic inference.

---

## Task Design Overview

Each instance provides the model with:
- a short video depicting object motion, and
- **one physical prior** in world units (object size, velocity at a given timestamp, or acceleration at a given timestamp).

The model is then asked to infer a target kinematic quantity—possibly for a different object—expressed in real-world units.

Tasks vary along four axes:
1. **Physical prior**: Size (S), Velocity (V), Acceleration (A)
2. **Dimensionality**: 2D (planar motion) or 3D (with depth variation)
3. **Object setting**: Single-object (S) or multi-object (M)
4. **Background complexity**: Plain (X), Simple (S), Complex (C)

---

## Validation Set Statistics

- **159 QA pairs**
- Covers all three physical priors (S / V / A)
- Includes both 2D and 3D configurations
- Videos sourced from:
  - Blender simulations,
  - laboratory captures,
  - curated internet videos

This subset is designed to be **representative but non-exhaustive** relative to the full benchmark.

---

## Data Sources and Quality Control

- **Simulation**: Blender-rendered scenes with precise physical ground truth.
- **Laboratory capture**: Real-world recordings using calibrated depth and multi-view setups.
- **Internet / author-recorded videos**: Carefully curated monocular videos meeting strict physical constraints.

All videos undergo manual review to remove:
- excessive motion blur,
- severe occlusion,
- untrackable motion,
- personally identifiable information (PII).

---

## License

The **annotations and metadata** in this repository are released under the  
**Creative Commons Attribution 4.0 (CC BY 4.0)** license.

Videos originate from simulated environments, laboratory recordings, and publicly available sources.  
Each video remains subject to its original license and terms of use.

This release is intended for **research and evaluation purposes**.

---

## Authors

**Puyin Li\***, **Tiange Xiang\***, **Ella Mao\***,  
Shirley Wei, Xinye Chen, Adnan Masood,  
Li Fei-Fei†, Ehsan Adeli†  

\* Equal contribution.

---

## Citation

If you use this validation set in your work, please cite:

```bibtex
@article{li2025quantiphy,
  title   = {QuantiPhy: A Quantitative Benchmark Evaluating Physical Reasoning Abilities of Vision-Language Models},
  author  = {Li, Puyin and Xiang, Tiange and Mao, Ella and Wei, Shirley and Chen, Xinye and Masood, Adnan and Li, Fei-Fei and Adeli, Ehsan},
  journal = {arXiv preprint arXiv:2512.19526},
  year    = {2025}
}